Side-information in machine learning is a very general term used in very different learning scenarios with quite different
connotations. Nevertheless, generally it is understood as any type of information, other than the learning instances, which can be
used to support the learning process; typically such information will live in a different space than the learning instances. Examples
include learning with privileged information [1]} in which during training a teacher provides additional information
for the learning instances; this information is not available in testing. In metric learning and clustering, it has been used to denote the
availability of additional similarity information on instances, i.e. pairs of similar and dissimilar instances, [2]}.
In this paper we focus on side-information describing the features.
We will consider learning problems in which we have additional information describing the properties and/or the relations of the
features. The features will have their own vectorial descriptions in some space in which we will describe their
properties.
